different results from lme and lmer function

Ben,
I doubt the very small difference in log likelihood gives much, if any
information about which model is a better fit. Even if we overlook the
limited precision of the estimate of the REML criterion, the difference is
so small as to me of minimal importance.
John

John David Sorkin M.D., Ph.D.

Professor of Medicine

Chief, Biostatistics and Informatics

University of Maryland School of Medicine Division of Gerontology and
Geriatric Medicine

Baltimore VA Medical Center

10 North Greene Street

GRECC (BT/18/GR)

Baltimore, MD 21201-1524

(Phone) 410-605-7119

(Fax) 410-605-7913 (Please call phone number above prior to faxing)


On May 26, 2015, at 8:03 PM, Ben Bolker <bbolker at gmail.com> wrote:

 These actually aren't terribly different from each other.  I suspect
that lmer is slightly closer to the correct answer, because lme
reports a "log-likelihood" (really -1/2 times the REML criterion) of
49.30021, while lmer reports a REML criterion of  -98.8 -> slightly
better fit at -R/2 = 49.4.  The residual sds are 0.0447 (lme) vs.
0.0442 (lmer); the intercept sd estimate is 0.016 vs 0.0089,
admittedly a bit low, and both month sds are very small.  lmer
indicates a singular fit (correlation of -1).    If you look at the
confidence intervals on these estimates (confint(fitted_model) in
lme4; intervals(fitted_model) in lme) I think you'll find that the
confidence intervals are much wider than these differences (you may
even find that lme reports that it can't give you the intervals
because the Hessian [curvature] matrix is not positive definite).

 Both should be comparable to SAS PROC MIXED results, I think, if
you get the syntax right ...

On Tue, May 26, 2015 at 7:09 PM, li li <hannah.hlx at gmail.com> wrote:

Hi all,

 I am fitting a random slope and random intercept model using R. I

used both lme and lmer funciton for the same model. However I got

different results as shown below (different variance component

estimates and so on). I think that is really confusing. They should

produce close results. Anyone has any thoughts or suggestions. Also,

which one should be comparable to sas results?

Thanks!

 Hanna


## using lme function

mod_lme <- lme(ti  ~ type*months, random=~ 1+months|lot, na.action=na.omit,

+ data=one, control = lmeControl(opt = "optim"))

summary(mod_lme)

Linear mixed-effects model fit by REML

Data: one

       AIC       BIC   logLik

 -82.60042 -70.15763 49.30021


Random effects:

Formula: ~1 + months | lot

Structure: General positive-definite, Log-Cholesky parametrization

           StdDev       Corr

(Intercept) 8.907584e-03 (Intr)

months      6.039781e-05 -0.096

Residual    4.471243e-02


Fixed effects: ti ~ type * months

                    Value   Std.Error DF   t-value p-value

(Intercept)     0.25831245 0.016891587 31 15.292373  0.0000

type            0.13502089 0.026676101  4  5.061493  0.0072

months          0.00804790 0.001218941 31  6.602368  0.0000

type:months -0.00693679 0.002981859 31 -2.326329  0.0267

Correlation:

              (Intr) typPPQ months

type           -0.633

months         -0.785  0.497

type:months  0.321 -0.762 -0.409


Standardized Within-Group Residuals:

         Min            Q1           Med            Q3           Max

-2.162856e+00 -1.962972e-01 -2.771184e-05  3.749035e-01  2.088392e+00


Number of Observations: 39

Number of Groups: 6





###Using lmer function

mod_lmer <-lmer(ti  ~ type*months+(1+months|lot), na.action=na.omit,
data=one)

summary(mod_lmer)

Linear mixed model fit by REML t-tests use Satterthwaite approximations to

 degrees of freedom [merModLmerTest]

Formula: ti ~ type * months + (1 + months | lot)

  Data: one


REML criterion at convergence: -98.8


Scaled residuals:

   Min      1Q  Median      3Q     Max

-2.1347 -0.2156 -0.0067  0.3615  2.0840


Random effects:

Groups   Name        Variance  Std.Dev.  Corr

lot      (Intercept) 2.870e-04 0.0169424

         months      4.135e-07 0.0006431 -1.00

Residual             1.950e-03 0.0441644

Number of obs: 39, groups:  lot, 6


Fixed effects:

               Estimate Std. Error        df t value Pr(>|t|)

(Intercept)     0.258312   0.018661  4.820000  13.842 4.59e-05 ***

type         0.135021   0.028880  6.802000   4.675  0.00245 **

months          0.008048   0.001259 11.943000   6.390 3.53e-05 ***

type:months -0.006937   0.002991 28.910000  -2.319  0.02767 *

---

Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1


Correlation of Fixed Effects:

           (Intr) typPPQ months

type     -0.646

months      -0.825  0.533

type:month  0.347 -0.768 -0.421